# RFT Memory Receipt Engine - a Hugging Face Space by RFTSystems

**URL:** <https://discuss.huggingface.co/t/rft-memory-receipt-engine-a-hugging-face-space-by-rftsystems/172169>\
**Category:** Spaces\
**Created:** [January 2, 2026, 4:07pm UTC](https://discuss.huggingface.co/t/rft-memory-receipt-engine-a-hugging-face-space-by-rftsystems/172169 "2026-01-02T16:07:33Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![RFTSystems](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/rftsystems/32/59206_2.png) [@RFTSystems](https://discuss.huggingface.co/u/RFTSystems)\
**Post date:** [January 2, 2026, 4:07pm UTC](https://discuss.huggingface.co/t/rft-memory-receipt-engine-a-hugging-face-space-by-rftsystems/172169/1 "2026-01-02T16:07:33Z")

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> **[RFT Memory Receipt Engine - a Hugging Face Space by RFTSystems](https://huggingface.co/spaces/RFTSystems/RFT_Memory_Receipt_Engine)**
>
> Discover amazing ML apps made by the community

I don’t do “trust me.” I do receipts. That’s why I built **RFT Memory Receipt Engine** — a local agent memory layer that generates a **verifiable receipt on every assistant turn** , showing exactly which memory slices were retrieved and proving they exist in a tamper-evident, append-only ledger.

What my space demonstrates: -  
**Durable session memory** via append-only event logs - **Fast lexical retrieval** using SQLite FTS (no embeddings required) - **Hash-chained ledger** so edits/reordering don’t silently pass - **Receipts per turn** containing: retrieved event IDs/snippets + digests + chain hashes + prompt hash + response hash - **Receipt verification** : upload a receipt back into the Space and it confirms the hashes match real stored events Why this matters: most “memory” systems store chat logs and call it solved. The real problem is **provable influence** — when someone asks “what did the agent actually use to decide that?”, you need evidence, not vibes. Receipts make agent memory **inspectable, auditable, and defensible**.

Quick test prompts (copy/paste): -

`Store this: Dog=Nova, City=Manchester, Drink=Pepsi Max.` - `What’s my dog’s name?` - `What city did I say?` - `My drink is Coke Zero now. This overrides earlier.` - `What’s my favourite drink?`

Then download the receipt JSON and upload it into **Verify Receipt**. It should pass. Edit one character in the receipt and it should fail.

I encourage any feedback around What extra fields would make receipts more useful for audits/debugging? - Should I add optional semantic retrieval (embeddings) as a toggle, or keep it lexical-only for maximum reliability? - Any agent framework integrations you’d want next (LangGraph / CrewAI / custom?

Liam-RFTsystems